Denoising of digital images through PSO based pixel classification

Somnath Mukhopadhyay 1  and Jyotsna Mandal 2
  • 1 Dept. of Computer Science & Engineering, Aryabhatta Institute of Engineering & Management, West Bengal, 713148, Durgapur, India
  • 2 Dept. of Computer Science & Engineering, University of Kalyani, West Bengal, 741235, Kalyani, India

Abstract

This paper proposes a de-noising method where the detection and filtering is based on unsupervised classification of pixels. The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function the image pixels are classified using the Particle Swarm Optimization (PSO) technique. The distance function measured similarity/dissimilarity among pixels using not only the intensity values, but also the positions of the pixels. The detection technique enforced PSO based clustering, which is very simple and robust. The filtering operator restored only the noisy pixels keeping noise free pixels intact. Four types of noise models are used to train the digital images and these noisy images are restored using the proposed algorithm. Results demonstrated the effectiveness of the proposed technique. Various benchmark images are used to produce restoration results in terms of PSNR (dB) along with other parametric values. Some visual effects are also presented which conform better restoration of digital images through the proposed technique.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] D. R. K. Brownrigg, The Weighted Median Filter, Commun. ACM 27(8), 807, 1984 http://dx.doi.org/10.1145/358198.358222

  • [2] O. Yli-Harja, J. Astola, Y. Neuvo, Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation, IEEE T. Signal Process. 39(2), 395, 1991 http://dx.doi.org/10.1109/78.80823

  • [3] S. J. KO, Y. H Lee, Center Weighted Median Filters and Their Applications to Image Enhancement, IEEE T. Circuit. Syst. 38(9), 984, 2001 http://dx.doi.org/10.1109/31.83870

  • [4] T. Chen, H. R. Wu, Adaptive Impulse Detection Using Center Weighted Median Filters, IEEE Signal Precess. Lett. 8(1), 1, 2001 http://dx.doi.org/10.1109/97.889633

  • [5] V. Crnojevic, V. Senk, Z. Trpovski, Advanced impulse Detection based on Pixel-Wise MAD, IEEE Signal Process. Lett. 11(7), 589, 2004 http://dx.doi.org/10.1109/LSP.2004.830117

  • [6] T. Chen, K. K. Ma, L. H. Chen, Tri- state median filter for image de noising, IEEE T. Image Process. 8(12), 1834, 1999 http://dx.doi.org/10.1109/83.806630

  • [7] T. Chen, H. R. Wu, Space variant Median Filters for the Restoration of Impulse noise Corrupted Images, IEEE T. Circuits-II 48(8), 784, 2001 http://dx.doi.org/10.1109/82.959870

  • [8] Z. Wang, D. Zhang, Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images, IEEE T. Circuits Syst. 46(1), 78, 1999

  • [9] E. Abreu, M. Lightstone, S. K. Mitra, K. Arakawa, A New Efficient Approach for the Removal of Impulse Noise from Highly corrupted Images, IEEE T. Image Process. 5(6), 1012, 1996 http://dx.doi.org/10.1109/83.503916

  • [10] Y. Dong, S. Xu, A New Directional Weighted Median Filter for Removal of Random — Valued Impulse Noise, IEEE Signal Process. Lett. 14(3), 193, 2007 http://dx.doi.org/10.1109/LSP.2006.884014

  • [11] Pankaj Kumar Sa, Ratnakar Dash, Banshidhar Majhi, Second order difference based Detection and Directional weighted median filter for removal of Random valued impulsive noise, International Conference on Industrial and Information Systems (ICIIS), Sri Lanka, December, 2009 (IEEE, 2009)

  • [12] J. K. Mandal, A. Sarkar, A modified weighted based filter for removal of random impulse noise, IEEE Second International Conference on Emerging Applications of Information Technology, 173, Kolkata, India, 2011 (IEEE, USA, 2011)

  • [13] J. K. Mandal, S. Mukhopadhyay, A Novel Technique for Removal of Random Valued Impulse Noise using All Neighbor Directional Weighted Pixels, Proceedings of the International Conference on Communications in Computer and Information Science (CCIS) Tirunelveli, India, Sept. 23–25, 2011 (Springer, Berlin, Heidelberg, 2011)

  • [14] J. K. Mandal, S. Mukhopadhyay, Edge Preserving Restoration of Random Valued Impulse Noises (EPRRVIN), Proceedings of International Conference on Recent Trends in Information Systems-RETIS, Kolkata, India, Dec. 21–23 2011 (IEEE, USA, 2011)

  • [15] J. K. Mandal, S. Mukhopadhyay, A Novel Variable Mask Median Filter for Removal of Random Valued Impulses in Digital Images (VMM), Proceedings of International Symposium on Electronic System Design -ISED, Kochi, Kerala, Dec. 19–21 2011 (IEEE, USA, 2011)

  • [16] J. K. Mandal, S. Mukhopadhyay, GA Based Denoising of Impulses (GADI), Proceedings of the International Conference on Communications in Computer and Information Science (CCIS), Kolkata, India, Dec. 14–16, 2011 (Springer, Berlin, Heidelberg, 2011)

  • [17] J. K. Mandal, S. Mukhopadhyay, Image filtering using all neighbor directional weighted pixels: optimization using particle swarm optimization, Int. J. Signal Image Process. 2(4), 187, 2011 http://dx.doi.org/10.5121/sipij.2011.2416

  • [18] J. K. Mandal, S. Mukhopadhyay, PSO Based Edge Keeping Suppression of Impulses in Digital Imagery, Proceedings of the International Conference on InConINDIA 2012, AISC, Visakhapatnam, India, Jan. 2012 (Springer, Berlin, Heidelberg, 2012)

  • [19] K. S. Srinivasan, D. Ebenezer, A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises, IEEE Signal Process. Lett. 14(3), 189, 2007 http://dx.doi.org/10.1109/LSP.2006.884018

  • [20] Madhu S. Nair, K. Revathy, R. Tatavarti, An improved decision based algorithm for impulse noise removal, Proceedings of 2008 International Congress on Image and Signal Processing-CISP, Sanya, Hainan, May 27–30 2008, (IEEE, USA, 2008)

  • [21] Pei-Eng Ng, Kai-Kuang Ma, A switching median filter with boundary discriminative noise detection for extremely corrupted images, IEEE T. Image Process. 16(6), 1506, 2006

  • [22] A. Nasimudeen, Madhu S. Nair, R. Tatavarti, Directional switching median filter using boundary discriminative noise detection by elimination, Signal, Image and Video Processing (Springer, London, 2012)

  • [23] Madhu S. Nair, G. Raju, A new fuzzy-based decision algorithm for high-density impulse noise removal, Signal, Image and Video Processing (Springer-Verlag, London, 2012)

  • [24] N. Thanh Binh, A. Khare, Adaptive Complex Wavelet Technique for Medical Image Denoising, ICDBME in Vietnam, IFMBE Proceedings, Ho Chi Minh City, Vietnam, Jan. 11–14 2010 (Springer, Berlin, Heidelberg, 2010)

  • [25] R. Dutra da Silva, R. Minetto, W. R. Schwartz, H. Pedrini, Adaptive edge-preserving image denoising using wavelet transforms, Pattern. Anal. Appl. 16(4), 567, 2012 http://dx.doi.org/10.1007/s10044-012-0266-x

  • [26] J.K. Mandal, S. Mukhopadhyay, Adaptive Median Filtering Based on Unsupervised Classification of Pixels, Handbook of Research on Computational Intelligence for Engineering, Science, and Business. IGI Global (IGI Global, USA, 2013)

  • [27] J. Kennedy, R. Eberhart, Particle Swarm Optimization, Proceedings of the IEEE International Joint Conference on Neural Networks, Perth, Australia, Nov./Dec. 1995 (IEEE, USA, 1995)

  • [28] M. Omran, A. Engebrecht, A. Salman, Particle Swarm Optimization method for image clustering, Int. J. Pattern Recogn. 19, 297, 2005 http://dx.doi.org/10.1142/S0218001405004083

  • [29] A. A. A. Esmin, D. L. Pereira, F. P. A. de Araújo, Study of different approach to clustering data by using particle swarm optimization algorithm, P IEEE World Congress on Evolutionary Computation (CEC 2008), Hong Kong, China, June 2008 (IEEE, USA, 2008)

  • [30] M. T. Wong, X. He, W.-C. Yeh, Image clustering using Particle Swarm Optimization, Evolutionary Computation (CEC), 2011 IEEE Congress on (IEEE, USA, 2011)

OPEN ACCESS

Journal + Issues

Open Computer Science is an open access, peer-reviewed journal. The journal publishes research results in the following fields: algorithms and complexity theory, artificial intelligence, bioinformatics, networking and security systems,
programming languages, system and software engineering, and theoretical foundations of computer science.

Search